In this paper, we propose a new measure to estimate the similarity between brands via posts of brands' followers on social network services (SNS). Our method was developed with the intention of exploring the brands that customers are likely to jointly purchase. Nowadays, brands use social media for targeted advertising because influencing users' preferences can greatly affect the trends in sales. We assume that data on SNS allows us to make quantitative comparisons between brands. Our proposed algorithm analyzes the daily photos and hashtags posted by each brand's followers. By clustering them and converting them to histograms, we can calculate the similarity between brands. We evaluated our proposed algorithm with purchase logs, credit card information, and answers to the questionnaires. The experimental results show that the purchase data maintained by a mall or a credit card company can predict the co-purchase very well, but not the customer's willingness to buy products of new brands. On the other hand, our method can predict the users' interest on brands with a correlation value over 0.53, which is pretty high considering that such interest to brands are high subjective and individual dependent.

中文翻译：

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使用社交媒体中的追随者帖子衡量品牌之间的相似性

在本文中，我们提出了一种新方法，通过社交网络服务（SNS）上的品牌追随者帖子来估计品牌之间的相似性。我们的方法旨在探索客户可能共同购买的品牌。如今，品牌使用社交媒体进行定向广告，因为影响用户的偏好可以极大地影响销售趋势。我们假设 SNS 上的数据允许我们在品牌之间进行定量比较。我们提出的算法会分析每个品牌粉丝发布的每日照片和主题标签。通过对它们进行聚类并将它们转换为直方图，我们可以计算品牌之间的相似度。我们通过购买日志、信用卡信息和问卷回答来评估我们提出的算法。实验结果表明，商场或信用卡公司维护的购买数据可以很好地预测共同购买，但不能很好地预测客户购买新品牌产品的意愿。另一方面，我们的方法可以预测用户对品牌的兴趣，相关值超过 0.53，考虑到对品牌的这种兴趣具有高度的主观性和个人依赖性，这是相当高的。